{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matplotlibに日本語フォントのインストール\n",
    "\n",
    "* http://ipafont.ipa.go.jp/old/ipafont/download.html\n",
    "    * ipag00303.zipをダウンロード\n",
    "* 解凍してできたipag.ttcを~/.fontsディレクトリに移動 (Ubuntu)\n",
    "    * 例\n",
    "        * mkdir ~/.fonts\n",
    "        * unzip ipag00303.zip\n",
    "        * mv ipag00303/ipag.ttf ~/.fonts\n",
    "* matplotlibrcの末尾に「font.family : IPAGothic」や「font.family : TakaoGothic」等を追記する\n",
    "    * 例\n",
    "        * vi /usr/local/pyenv/versions/3.6.1/envs/jupyter/lib/python3.6/site-packages/matplotlib/mpl-data/matplotlibrc\n",
    "* ユーザのキャッシュを削除する\n",
    "    * 例\n",
    "        * rm -i ~/.cache/matplotlib/*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/usr/local/pyenv/versions/3.5.4/envs/jupyter/lib/python3.5/site-packages/matplotlib/mpl-data/matplotlibrc'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### [参考]matplotlibrcの場所\n",
    "import matplotlib\n",
    "matplotlib.matplotlib_fname()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/usr/share/fonts/truetype/fonts-japanese-mincho.ttf',\n",
       " '/usr/share/fonts/truetype/takao-mincho/TakaoPMincho.ttf',\n",
       " '/usr/share/fonts/truetype/takao-gothic/TakaoPGothic.ttf',\n",
       " '/usr/share/fonts/truetype/takao-mincho/TakaoMincho.ttf',\n",
       " '/usr/share/fonts/truetype/takao-gothic/TakaoGothic.ttf',\n",
       " '/usr/share/fonts/truetype/dejavu/DejaVuSerif.ttf',\n",
       " '/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf',\n",
       " '/usr/share/fonts/truetype/dejavu/DejaVuSansMono-Bold.ttf',\n",
       " '/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf',\n",
       " '/usr/share/fonts/truetype/dejavu/DejaVuSerif-Bold.ttf',\n",
       " '/usr/share/fonts/truetype/fonts-japanese-gothic.ttf',\n",
       " '/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### [参考]有効なフォント一覧\n",
    "import matplotlib.font_manager as fm\n",
    "import pandas as pd\n",
    "fonts = fm.findSystemFonts()\n",
    "fonts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>name</th>\n",
       "      <th>family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/usr/share/fonts/truetype/takao-mincho/TakaoPM...</td>\n",
       "      <td>TakaoPMincho</td>\n",
       "      <td>[TakaoGothic]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/usr/share/fonts/truetype/takao-gothic/TakaoPG...</td>\n",
       "      <td>TakaoPGothic</td>\n",
       "      <td>[TakaoGothic]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/usr/share/fonts/truetype/takao-mincho/TakaoMi...</td>\n",
       "      <td>TakaoMincho</td>\n",
       "      <td>[TakaoGothic]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/usr/share/fonts/truetype/takao-gothic/TakaoGo...</td>\n",
       "      <td>TakaoGothic</td>\n",
       "      <td>[TakaoGothic]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                path          name  \\\n",
       "1  /usr/share/fonts/truetype/takao-mincho/TakaoPM...  TakaoPMincho   \n",
       "2  /usr/share/fonts/truetype/takao-gothic/TakaoPG...  TakaoPGothic   \n",
       "3  /usr/share/fonts/truetype/takao-mincho/TakaoMi...   TakaoMincho   \n",
       "4  /usr/share/fonts/truetype/takao-gothic/TakaoGo...   TakaoGothic   \n",
       "\n",
       "          family  \n",
       "1  [TakaoGothic]  \n",
       "2  [TakaoGothic]  \n",
       "3  [TakaoGothic]  \n",
       "4  [TakaoGothic]  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### [参考]familyの確認\n",
    "l = []\n",
    "for f in fonts:\n",
    "    font = fm.FontProperties(fname=f)\n",
    "    l.append((f, font.get_name(), font.get_family()))\n",
    "df = pd.DataFrame(l, columns=['path', 'name', 'family'])\n",
    "#df[df['path'].apply(lambda s: 'ipa' in s)]\n",
    "df[df['path'].apply(lambda s: 'takao' in s)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# グラフ化に必要なものの準備\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Jupyter上に図を表示するためのおまじない\n",
    "%matplotlib inline\n",
    "\n",
    "# データの扱いに必要なライブラリ\n",
    "import pandas as pd\n",
    "\n",
    "# チャートがきれいになるおまじない\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>徳島</th>\n",
       "      <th>鳴門</th>\n",
       "      <th>阿南</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            徳島  鳴門  阿南\n",
       "Date                  \n",
       "2020-01-01   5   6   3\n",
       "2020-01-02   6   8   4\n",
       "2020-01-03   9  10   6\n",
       "2020-01-04   4   5   2\n",
       "2020-01-05   5   6   3"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### csvファイルのPATHを設定\n",
    "csv_path='csv/Temperature.csv'\n",
    "\n",
    "### テスト用ファイルの作成\n",
    "strs = \"\"\"Date,Tokushima,Naruto,Anan\n",
    "2020-01-01,5,6,3\n",
    "2020-01-02,6,8,4\n",
    "2020-01-03,9,10,6\n",
    "2020-01-04,4,5,2\n",
    "2020-01-05,5,6,3\n",
    "\"\"\"\n",
    "f = open(csv_path, 'w')\n",
    "f.writelines(strs)\n",
    "f.close\n",
    "\n",
    "### プロットするデータの整形\n",
    "df = pd.read_csv(csv_path, index_col=0)\n",
    "df = df.iloc[:, [0, 1, 2]]\n",
    "df.columns = [u'徳島', u'鳴門', u'阿南']\n",
    "df.index = pd.to_datetime(df.index)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55f25329e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### データを描画\n",
    "df.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
